AIP-C01 · Question #3
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into…
The correct answer is A. Deploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply. The three problems are inconsistency, latency, and unsafe outputs. Provisioned throughput in Amazon Bedrock allocates dedicated model capacity, eliminating latency spikes caused by on-demand throttling - this directly addresses slow inference. To achieve 99.5% output…
Question
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer. Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls. Which solution will meet these requirements?
Options
- ADeploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply
- BUse Amazon Bedrock Agents to manage chaining. Log model inputs and outputs to Amazon
- CCache prompt results in Amazon ElastiCache. Use AWS Lambda functions to pre-process
- DUse Amazon Kendra to improve roast log retrieval accuracy. Store normalized prompt metadata
How the community answered
(57 responses)- A70% (40)
- B19% (11)
- C7% (4)
- D4% (2)
Explanation
The three problems are inconsistency, latency, and unsafe outputs. Provisioned throughput in Amazon Bedrock allocates dedicated model capacity, eliminating latency spikes caused by on-demand throttling - this directly addresses slow inference. To achieve 99.5% output consistency for identical inputs, the solution sets temperature to 0 (deterministic sampling), ensuring the model produces the same output for the same input every time. Amazon Bedrock Guardrails with safety filters block unsafe recommendations (e.g., dangerously high brewing temperatures). Together these three mechanisms address all three stated problems with a managed AWS service. Option C (ElastiCache caching) can help consistency but does not address safety or the root cause of latency. Option B (Bedrock Agents + logging) adds orchestration complexity without ensuring deterministic outputs. Option D (Kendra) improves retrieval but does not address output consistency or safety.
Topics
Community Discussion
No community discussion yet for this question.